Home Journals Articles Updates About
CN
image
收藏切换
Wheat straw and alfalfa hay alone or combined in a high-concentrate diet alters microbial-host interaction in the rumen of lambs
收藏切换
PDF
Kefyalew Gebeyewa, b, Hui Mia, b, Ruiping Duc, Min Gaoc, Diriba Dibad, Shaoxun Tanga, Zhixiong Hea, b, *, Zhiliang Tana, b
Animal Nutrition | 2025, 20(1) : 444 - 457
Less
收藏切换
Animal Nutrition | 2025, 20(1): 444-457
• Original Research Article •
Wheat straw and alfalfa hay alone or combined in a high-concentrate diet alters microbial-host interaction in the rumen of lambs
Full
Kefyalew Gebeyewa, b, Hui Mia, b, Ruiping Duc, Min Gaoc, Diriba Dibad, Shaoxun Tanga, Zhixiong Hea, b, *, Zhiliang Tana, b
Affiliations
  • aCAS Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
  • bUniversity of Chinese Academy of Sciences, Beijing 100049, China
  • cInner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot 010031, China
  • dDepartment of Animal Sciences, Faculty of Agriculture, Wollega University, Ethiopia
Published: 2025-03-10 doi: 10.1016/j.aninu.2024.08.010
Outline
收藏切换

The inclusion of various forages in a normal forage-to-concentrate ratio has widely been reported to reveal the changes that occur in the foregut tissues. However, the mechanism by which the wheat straw, alfalfa hay, or both alter the orchestrated crosstalk of microbiome and host-transcriptome in the rumen of lambs fed a high-concentrate diet is elusive. Sixty-three Hulunbuir lambs were randomly allotted to 3 dietary groups, and each dietary group had 3 pens with 7 lambs. The lambs were fed high-concentrate diets (70%) supplemented with either 30% wheat straw (30S), a mixture of 15% alfalfa hay and 15% wheat straw (30M), or 30% alfalfa hay (30A) over a 2-week adaptation period and a 12-week formal trial. Compared with the 30S and 30A groups, the 30M group had greater (P < 0.05) levels of plasma glucagon-like peptide (GLP-2), interleukin-2 (IL-2). Humoral immunity showed a tendency to increase in the 30M group, as evidenced by the greater levels of plasma immunoglobulins (Ig) A and IgG (P > 0.05). The 16S rRNA result showed that the abundance of Lachnospiraceae (NK3A20 group and unclassified), Olsenella, Shuttleworthia, and Succiniclasticum were enriched in the 30M group. Meanwhile, the abundances of Ruminococcaceae NK4A214 and prevetolla_7 were enriched in 30S and 30A, respectively. The RNA-seq identified 35 shared differentially expressed genes (DEGs) between the "30S vs. 30M" and "30S vs. 30A," enriched in lipid metabolism pathways, including glycerophospholipid and arachidonic acid metabolism. The weighted gene co-expression network analysis results revealed that the expression of genes in the darkred (194 genes) and darkgreen (134 genes) modules showed a strong positive correlation with phenotypic traits and bacterial genera, respectively. The genes in the darkgreen module were involved in carbohydrate, lipid, and amino acid metabolism and showed a wide range of associations with Prevotella_7, Shuttleworthia, and Succiniclasticum, indicating that ruminal microbes might act as a vital driver in the microbiome-host interaction, likely through fermentation of end-products or metabolites. In conclusion, the results indicate that microbiome enrichment in response to feeding wheat straw and alfalfa hay might drive microbiome-host crosstalk to regulate rumen function in lambs fed a high-concentrate diet.

High-concentrate diet  /  Interaction  /  Lamb  /  Microbiome  /  Rumen
Kefyalew Gebeyew, Hui Mi, Ruiping Du, Min Gao, Diriba Diba, Shaoxun Tang, Zhixiong He, Zhiliang Tan. Wheat straw and alfalfa hay alone or combined in a high-concentrate diet alters microbial-host interaction in the rumen of lambs[J]. Animal Nutrition, 2025 , 20 (1) : 444 -457 . DOI: 10.1016/j.aninu.2024.08.010
Full Text
Less
1.　Introduction
Less
With the desire to achieve higher production performance and profitability, feeding energy-rich diets or high-concentrate diets for fattening or growing ruminants has become a common trend in the livestock industry. These feeding patterns may cause adverse effects on rumen function, such as lower pH, increased accumulation of volatile fatty acids (VFAs), and decreased fiber degradation, leading to a higher risk of various diseases or metabolic disorders (Elmhadi et al., 2022). In this regard, Angus cows fed a 65% concentrate diet showed greater dry matter intakes and growth performance at the cost of health disorders, revealed by a high concentration of rumen and plasma lipopolysaccharide (Chen et al., 2021). Furthermore, feeding high-concentrate diets has also been found to affect the rumen microbial community structure and function, as well as its interaction with the host animal. These shifts in microbial structure or composition in response to feeding a high-concentrate diet might cause a loss of functional capacity at the community level (McCann et al., 2016; Mu et al., 2022). Studies have also found that feeding high-concentrate diets depleted bacterial diversity and reduced functionality of the rumen, as evidenced by changes in the abundance and functional role of various bacterial genera (Khafipour et al., 2009). Such changes in microbial structure and function are also likely to alter host metabolic function.
The rumen microbiota plays a crucial role in regulating host metabolism, and it's estimated to influence over 10% of the host transcriptome (Malmuthuge et al., 2019). This regulation likely occurs through complex interactions between microbial and host metabolism, which evolve throughout different growth stages and in response to dietary supplementation. On top of that, the microbially-driven VFAs, such as butyrate and acetate, have been shown to mediate the biological processes essential for the growth of ruminal epithelium in lambs fed a starter diet (Lin et al., 2019a). This underscores the potential for dietary modifications to shape microbiome-host crosstalk and thereby influence various physiological functions. Furthermore, studies have also shown that goat kids fed a diet containing milk replacer with concentrate and alfalfa hay improved rumen papillae growth. This improvement was linked to changes in the microbiota composition, which affected gene expression involved in both rumen development and VFA production (Chai et al., 2021). Despite these insights, knowledge of microbial-host interaction and its impacts on host metabolism primarily focuses on starter diets or specific developmental stages. The underlying mechanism of how feeding a high-concentrate diet with wheat straw or alfalfa hay alters the microbial-host interaction and host metabolism function at a molecular level is unclear.
The inclusion of various forages as a source of neutral detergent fiber (NDF) in the diets of dairy cows has become trendy as part of promoting production and a healthy gastrointestinal environment. In this sense, feeding equivalent contents of NDF from various forages to dairy cows has shown comparable results in milk yield and digestibility (Eastridge et al., 2017). Likewise, feeding wheat straw and corn stover to dairy cows has more pronounced effects on intake, pH, and energy balance than corn stover and alfalfa hay in diets formulated to provide equivalent levels of undigested NDF (Kahyani et al., 2019). Furthermore, the inclusion of different levels of corn stover as fiber in high- and low-concentrate diets of heifers improved microbial protein synthesis and dry matter intake without affecting body weight (BW) (Lascano and Heinrichs, 2011). These results have indicated the contribution of fiber from various forages in modulating the rumen environment and phenotypic traits. To this end, replacing wheat straw with alfalfa hay in a concentrate-rich diet results in variable levels of potentially digestible and indigestible NDF and protein. Thus, we hypothesized that the differences in nutrient composition in the supplemented forages might have different effects on the phenotypic traits and crosstalk of ruminal microbial-host metabolism to regulate the rumen function in lambs fed a high-concentrate diet. Furthermore, the rumen epithelium is believed to be a unique site for the crosstalk of microbe-host metabolism due to its wide range of contributions to the growth of a given host. Thus, analysis of the host transcriptome and ruminal microbiome might offer deeper insight into the microbe-host interaction in lamb fed a high-concentrate diet with various forages. To achieve this, we identified the gene module that has a strong association with the bacterial taxa using the weighted correlation network analysis (WGCNA) approach.
2.　Materials and methods
Less
2.1.　 Animal ethics statement
The Institute of Subtropical Agriculture's Animal Care and Use Committee reviewed and approved all experimental protocols for this trial (ISA-202020). The lambs involved in the experiment were managed in accordance with these approved protocols. The feeding trial took place in Hulunbuir City, located in the Inner Mongolia Autonomous Region of China.
2.2.　 Animals and experimental design
Sixty-three fat-tailed male Hulunbuir lambs at 3 months of age with an initial mean BW of 18.55 ± 1.98 kg were allotted to 9 pens in a randomized complete block design based on initial BW. Pens were randomly assigned to 1 of the 3 treatment groups. The experimental diets were prepared with a forage-to-concentrate ratio of 30:70 on a dry matter (DM) basis. The lambs were fed a diet comprising 70% barley and corn-based concentrate supplemented with either 30% wheat straw (30S), a combination of 15% alfalfa hay and 15% wheat straw (30M), or 30% alfalfa hay (30A). The feeding trial was conducted for 14 weeks, including 2 weeks of acclimatization and a 12-week formal trial. The nutrient levels of the experimental diets are presented in Table 1. Lambs received their respective diets thrice daily at 07:30, 14:30, and 20:30 and had free access to water. The BW was taken at 2-week intervals before morning feeding, and average daily gain (ADG) was calculated as the slope of BW against time. Feed offered and refusals per pen were recorded daily to calculate the individual feed intake (IFI) during the formal trial period. The ratio method was implemented to compute the IFI as described elsewhere (Lee et al., 2016).
2.3.　 Sample collection
On day 90 before morning feeding, blood was drawn from the jugular vein of each lamb into 5-mL heparinized tubes (Changsha Yiqun Medical Equipment Co., Ltd., Hunan, China) to measure the concentrations of cytokines and immunoglobulins. After centrifugation at 3000 × g for 15 min at room temperature, the aliquots were collected and frozen at −80 °C until analysis. Seven lambs from each group with a final BW close to the group mean BW were selected, slaughtered by a registered veterinarian, and immediately eviscerated. Following dissection and separation of serosal and muscular layers, the rumen epithelial tissue was individually collected from the bottom side of the central sac, washed with sterile phosphate-buffered saline (pH 7.4), instantly frozen in liquid nitrogen, and stored at −80 °C for transcriptome analysis. Rumen contents (the mixed rumen digesta) were filtered using 4 layers of sterile cheesecloth, and about 10 mL of rumen content was aseptically collected into a sterile plastic tube, snap-frozen in liquid nitrogen, and stored at −80 °C for 16S rRNA sequencing.
2.4.　 Feed analysis
Experimental feed dry matter (DM, method 930.15), crude protein (CP, N × 6.25; method 984.13), ash (method 942.05), and calcium (Ca) and phosphorus (P) (method 935.13) were assessed using the standard protocol of the Association of Official Agricultural Chemists (AOAC, 2000). Acid-detergent fiber (ADF) and NDF of the experimental feed were measured following the methods of Van Soest et al. (1991).
2.5.　 Detection of plasma cytokines and immunoglobulins
The plasma levels of glucagon-like peptide 2 (GLP-2), interleukin-2 (IL-2), transforming growth factor β (TGF-β), tumor necrosis factor-α (TNF-α), interferon-γ (INF-γ), immunoglobulin A (IgA), IgG, and IgM were detected using the sheep enzyme-linked immunosorbent assay (ELISA) kits following the manufacturer's directions (Jiangsu Meimian Industrial Co., Ltd., Yancheng, China). The levels of immunoglobulins and cytokines were expressed as ng/mL, μg/mL, or pg/mL. The intra-assay and inter-assay coefficient variation (CV) of ELISA kits used for cytokines and immunoglobulins were <10% and <12%, respectively.
2.6.　 Genomic DNA extraction and 16S rDNA gene sequencing
The E.Z.N.A. Stool DNA Kit (D4015, Omega Bio-tek, Inc., Norcross, GA, USA) was used to isolate the microbial DNA from the rumen content biopsies following the manufacturer's guidelines. The quantity and purity of isolated DNA were verified using a NanoDrop 2000 UV–vis Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The universal primer set 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) along with 12 bp unique barcodes were used to amplify the bacterial 16S rRNA gene in the V3–V4 hypervariable region and to construct an amplicon library (Fadrosh et al., 2014). The PCR reactions were performed as described by Gebeyew et al. (2021). A 2% agarose gel electrophoresis was used to verify the PCR products. The AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) were then used to purify the PCR products. The Qubit 3.0 (Invitrogen, Waltham, MA, USA) was used to quantify the PCR products, which were pooled into 1 sample based on equimolar concentration to construct Illumina paired-end libraries. The quantity and size of the amplicon library were determined using the Illumina Library Quantification Kit (Kapa Biosciences, Woburn, MA, USA) and an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). The libraries were sequenced on the NovaSeq PE250 platform (Illumina Technologies Co., Ltd, San Diego, CA, USA).
2.7.　 Computational approaches for 16S rRNA sequencing data
The QIIME data analysis package (version 1.9.1) was used to perform the quality control of raw data in FASTQ files as described in our previous study (Yang et al., 2021). Clean reads were then clustered into operational taxonomic units (OTUs) at a 97% similarity cutoff using USEARCH (version 10). The representative read of each OTU was annotated and aligned to the SILVA 16S rRNA data-base (version 138.1) using the RDP classifier algorithm with a confidence threshold of 80% (Quast et al., 2013). The population richness (Chao1) and evenness (Shannon index) were computed using QIIME2 (available at https://library.qiime2.org/). Principal coordinate analysis (PCoA) based on the weighted UniFrac distance was used to assess beta diversity across the 3 treatment groups.
2.8.　 RNA extraction, sequencing, and library preparation
Trizol reagent (Invitrogen, Carlsbad, CA, USA) was used to isolate total RNA from each rumen epithelium tissue as per the manufacturer's direction. The ND-1000 NanoDrop Spectrometer and Agilent 2100 bioanalyzer (Thermo Fisher Scientific, MA, USA) were used to quantify the total RNA concentration and purity, respectively. The RNA integrity was checked using agarose-formaldehyde (1%) gel electrophoresis. Samples with an RNA integrity number (RIN) value of >7 were used for RNA-seq library construction. TruSeq RNA Library Prep Kit v2 (Illumina, CA, USA) was used to construct the library for RNA seq and enrich poly(A)-tailed host mRNA with oligo(dT) beads. The BGIseq 500 platform was used to sequence the library at BGI Tech Solutions Co., Ltd. (BGI-Shenzhen, China) and generate single-end 50-base reads as per the manufacturer's directions.
2.9.　 Quality control analysis and mapping
The Trimmomatic module in SOAPnuke (v1.5.2) was used to filter the raw reads (Li et al., 2008). Raw reads were flagged as low-quality and excluded from the datasets if they: (1) contained an adapter; (2) had greater than 50% of ambiguous sequences labeled as N; (3) had more than 20% of bases with a quality score below 10. High-quality reads were mapped against the Ovis aries reference genome (Oar V3.1) using HISAT2 (v2.1.0) (Kim et al., 2015) with the default parameters. The expression levels of host genes were calculated based on the number of fragments per kilobase of exon per million fragments mapped (FPKM).
2.10.　 Identifying differentially expressed genes
The bioinformatics tool DESeq2 (v3.15) was used to identify the differentially expressed genes (DEGs) between the 3 dietary groups (Love et al., 2014). The DEGs were declared with log2 fold change (logFC) greater than 1 and adjusted P-values (using the Benjamini-Hochberg [BH] method for controlling the false discovery rate [FDR]) less than 0.05 (Benjamini and Hochberg, 1995). The DEGs were presented based on the pairwise comparison among the dietary groups (30S vs. 30M, 30S vs. 30A, and 30M vs. 30A).
2.11.　 WGCNA
We used WGCNA approaches to uncover the interaction of the lamb phenotypic traits (body weight changes [BWC], IFI, cytokines, and immunoglobulins) and metagenomics with the rumen tissue transcriptome (generated from the same samples) (Langfelder and Horvath, 2008). All expressed protein-coding genes (9322, FPKM >1.0) in rumen tissue samples from all experimental lambs were used in the WGCNA (R package v3.4.1). Briefly, the pickSoft-Threshold function in the WGCNA package was used to build a gene co-expression network according to the co-expression/correlation modes among genes. We generated 16 (RNA-seq vs. phenotype traits) and 15 (RNA-seq vs. bacterial genera) modules using the blockwiseModules function based on the following parameters: minimum module size of 20. Merged cutoff of 75%, and soft-thresholding powers of 10 and 7, respectively. And the identified clusters of densely interconnected genes (gene modules) were presented using a hierarchical clustering method. We then computed the Pearson correlation coefficients between the targeted traits and eigengenes of each gene module. The gene modules were declared statistically associated with the targeted traits at P < 0.05. Thus, we found that the darkred (RNA-seq vs. phenotype traits) and darkgreen (RNA-seq vs. bacterial genera) modules were highly associated with the phenotypic traits and bacterial genera, respectively, and these modules were used for further correlation analysis to identify the degree of association between respective traits and each gene module.
2.12.　 Association of plasma immune indexes and abundance of bacteria with the gene module
The bacterial genera and genes from the darkgreen module, as well as plasma cytokines and immunoglobulin, and genes from the darkred module — both negatively and positively associated with the respective traits — were subjected to correlation analysis. The analysis was performed using Spearman's rank correlation coefficient, and the results were visualized using a heatmap generated by the R package in the Omics studio (LC-Bio Technology Co., Ltd., Hangzhou, China). The correlation results with the P < 0.01 and |r| ≥ 0.6 were declared as statistically significant or strongly associated.
2.13.　 The functional analysis
Database for Annotation, Visualization, and Integrated Discovery (DAVID) (v2022q3) was used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment and Gene Ontology (GO) terms for the DEGs, the darkgreen and darkred modules, which highly correlated with lamb phenotype traits (Nishihara et al., 2023). The KEGG pathways and GO terms were declared as significantly enriched biological functions at P < 0.05.
2.14.　 Statistical analysis
Statistical analyses were applied using the SPSS version 23.0 (SPSS Inc., Chicago, IL, USA) and OriginPro 2023b. Each pen was used as an experimental unit for measuring BW and feed intake. Meanwhile, an individual lamb was considered as an experimental unit for the remaining variables, which were measured for each animal. All data were subjected to the analysis of variance (ANOVA) using the GLM procedure. First, the normality and homoscedasticity of data were verified using Shapiro–Wilk and Levene's tests, respectively. Then, the differences between group means were determined using Tukey's post hoc tests. Given that the relative abundances of bacterial phyla and genera were not normally distributed, we used the Wilcoxon rank-sum test to analyze the differential abundances between the dietary groups. The statistical differences and tendencies were regarded at P < 0.05 and 0.05 ≤ P < 0.10, respectively. The mean ± SEM was used to present the results.
The model used in the experiment was:
where Yijk was the response variable, μ was the grand mean, τi was the ith treatment effect, βj was the jth block effect, and εijk was the error term.
3.　Results
Less
3.1.　 Growth performance and immune profiles
As shown in Table 2, IFI and ADG were unaffected by the dietary treatments (P > 0.05). The 30M group had greater (P = 0.001) IL-2 compared with the 30S and 30A groups (Table 3), highlighting resistance to high-concentrated diet-induced inflammation as IL-2 has anti-inflammatory characteristics. The level of GLP-2 was greater (P = 0.007) in the 30M group than that in the 30S group but was comparable with the 30A group (P > 0.05). The levels of IgA and IgG tended to be higher in the 30M group than in the other 2 groups (P > 0.05), indicating feeding a mixture of alfalfa hay and wheat straw may improve humoral immunity. Levels of IgM showed no significant variation across the 3 groups (P = 0.218).
3.2.　 Ruminal microbiota profiles
The effects of wheat straw and alfalfa hay alone or combined on the profiles of ruminal bacterial taxa were investigated. The results of the rumen content microbiota analysis showed that neither the Chao1 index nor the Shannon index was affected by the dietary treatments (P > 0.05, Fig. S1). However, the compositions of ruminal phyla and genera were significantly altered across the 3 dietary treatments (Fig. 1). Notably, lambs in the 30M group had greater (P < 0.05) relative abundances of Firmicutes and Actino-bacteria and lower (P < 0.05) relative abundance of Proteobacteria than those in the other two dietary groups. The 30S group had a greater abundance of Fibrobacteres compared with the 30M (P = 0.082) and 30A (P < 0.05) groups. At the genera level, lambs in the 30M group had greater (P < 0.05) relative abundances of Eubacterium_coprostanol_genes_group, Lachnospiraceae NK3A20 group, Lachnospiraceae unclassified, and Olsenella than those in the 30S and 30A groups (Fig. 2). The 30A group had a lower abundance of Ruminococcaceae NK4A214 group compared with the 30S (P = 0.055) and 30M (P = 0.018) groups (Fig. 3). Conversely, lambs in the 30A group had (P = 0.037) a greater abundance prevetolla_7 than that in the 30S group but comparable with the 30M group (P > 0.05). Compared with the 30S group, the 30M group had a higher (P = 0.028) abundance of Shuttleworth, which was comparable (P > 0.05) to that in the 30A group. Lambs in the 30S group had a lower abundance of Succiniclasticum compared with the 30M (P = 0.065) and 30A (P = 0.037) groups.
3.3.　 Profiling the transcriptome of rumen epithelium
About 16,319 ± 93 genes expressed across all sample tissues across the 3 treatments were profiled using the RNA-seq-based transcriptome approach (Table S1). We observed a higher number of DEGs (FC > 1) when comparing between the 30S vs. 30A and 30S vs. 30M groups and a low number of DEGs between the 30M vs. 30A group (Table S2). Of them, 89 and 257 up-and-downregulated DEGs were identified in the 30A compared with the 30S group, respectively (Fig. 4A). Likewise, about 32 and 84 up-and-downregulated DEGs were detected in the 30M compared with the 30S group, respectively (Table S3). Equal numbers of up-and-down DEGs were observed between the 30M vs. 30A groups (Table S4). As shown in Fig. 4B, 109 and 80 unique DEGs were detected between the 30S vs. 30M and 30S vs. 30A groups, respectively, whereas 35 shared DEGs were also observed between 30S vs. 30M and 30S vs. 30A groups.
To identify the significantly enriched pathways associated with the unique and shared DEGs, we performed functional analysis using KEGG pathway enrichment analysis. The significantly enriched pathways for the unique DEGs between the 30S vs. 30A groups were the "glycolysis/gluconeogenesis (PGAM2 and HK2)," "biosynthesis of amino acids (PYCR1 and PGAM2)," "Fructose and mannose metabolism (PFKB3)," and "complement and coagulation cascades (MASP2 and C4BPA)" (Fig. 4C and F). Likewise, several pathways were significantly enriched for the common DEGs between the 30S vs. 30M and 30S vs. 30A, including "Glycerophospholipid metabolism" and "Arachidonic acid metabolism (PLA2G4D and PLA2G4B)" (Fig. 4D). The PLA2G4D and PLA2G4B genes were down-regulated by the supplementation of wheat straw and alfalfa hay mixture or alfalfa hay alone compared to the wheat straw alone (Fig. 4G). The significantly enriched pathways for unique DEGs between the 30S vs. 30M groups were "Biosynthesis of unsaturated fatty acids (ACOT7 and ELOVL4)," "Mineral absorption (SLC9A3 and SLC26A9)," NOD-like receptor signaling pathway (P2RX7, RNASEL, and TRPM2)", and "NF-kappa B signaling pathway (CARD14 and LBP)" (Fig. 4E–H).
3.4.　 The WGCNA revealed the host transcriptome interaction with the rumen microbiome and plasma immune indexes
To gain a better insight into the interaction between the host transcriptome and phenotypic traits and microbiome, we identified about 9322 genes as core transcriptome, using criteria that included genes expressed in all sample tissues and FPKM >1 across the 3 groups (Table S5). These genes were subjected to the WGCNA approach. The results highlighted that a total of 16 and 15 modules showed various degrees of association with the lamb's phenotypic traits (BWC, IFI, and plasma immune indexes) and the abundance of bacterial genera, respectively (Fig. 5A and B; Figs. S2 and 3). The expression of genes in the darkred (194 genes) and darkgreen (134 genes) modules showed a strong positive correlation with the respective traits (Tables S6–8). About 9 genes in the darkgreen and darkred modules overlapped with the DEGs, including SRI, ISYNA1, S100A14, ID1, PYCR1, ACOT8, ACOT7, WDR34, and TPT1. The functional analysis for the selected modules was performed using KEGG pathway enrichment analysis. The genes in the darkred module were enriched with biological functions, including "cell cycle," "aminoacyl-TRNA biosynthesis," "intestinal immune network for IgA production," "adipocytokine signaling pathway," and "AMPK signaling pathway" (Fig. 6A). The genes in the darkgreen module were related to amino acid, carbohydrate, and lipid metabolism, including "biosynthesis of amino acids," "amino sugar and nucleotide sugar metabolism," and "biosynthesis of unsaturated fatty acids," and "cysteine and methionine metabolism" (Fig. 6B). These major genes, associated with various pathways, showed different expression levels across the 3 dietary treatment groups (Fig. 7).
To further identify the degree of association of each gene from the darkred and darkgreen modules with the immune indexes and bacterial taxa, we performed Spearman's rank correlations of the gene from the selected modules with plasma cytokines and immunoglobulins or bacterial genera. The plasma level of IgA had strong correlations (P < 0.01) with SRA1, RBM8A, and RUVBL2 (Fig. S4). Likewise, the plasma level of GLP-2 showed a significant correlation (P < 0.01) with CCT7, OAZ1, LAMTOR2, MLF2, TMEM223, GALT, CDKZAP1. The abundance of Prevotella_7 had positive correlations (P < 0.01) with 48 genes, including GOT2, TECR, MPI, and CBS (Fig. S5). Likewise, the abundance of Shuttleworthia showed positive correlations (P < 0.01) with 48 genes, including PYCR1 and GMPPB. The abundance of Succiniclasticum had a positive association (P < 0.01) with 19 genes, including MIF and ACOT8. The abundance of Olsenella showed a positive association with CIAPIN1. The abundance of Prevotella_1 had negative correlations (P < 0.01) with NTMT1 and GPS1. The abundance of Rikenellaceae_RC9_gut_group showed negative correlations (P < 0.01) with MLF2, PGP, and PIH1D1 and a positive association with SMYD2. The abundance of the Ruminococcacee NK4A214_ group had a negative correlation (P < 0.01) with TRABD and a positive correlation (P < 0.01) with SMYD2.
4.　Discussion
Less
It is well documented that wheat straw has low protein and starch levels but a high NDF with a slow digestion rate. In contrast, alfalfa hay is characterized by a high proportion of protein, moderate starch levels, and digestible NDF with a high digestion rate (Kahyani et al., 2019). Thus, the experimental diets were planned to provide different levels of nutrients from wheat straw and alfalfa hay alone or combined in a concentrate-rich diet. These differences in nutrient compositions were supposed to modulate the rumen, thereby affecting phenotypic traits and molecular features, including growth performance and host-microbial consortia. However, a partial or full replacement of alfalfa hay for wheat straw in a high-concentrate diet did not significantly improve the growth performance and feed intake of lambs. The findings from the present study are in close agreement with the results of a previous study (Lascano and Heinrichs, 2011). In the cited study, both ADG and feed intake failed to show improvement in response to the inclusion of various levels of corn stover as a fiber source in concentrate-rich diets (forage-to-concentrate ratio of 20:80) in heifers. The results of our study can be explained by the small gap in fiber and starch compositions between wheat straw and alfalfa hay. Alternatively, the high proportion of concentrate in the total ration potentially limited the influence of forages on the phenotype traits. Feeding alfalfa hay as a replacement for wheat straw for an extended period is expected to result in noticeable differences in phenotypic traits. Despite a lack of substantial improvement in phenotypic traits, feeding alfalfa hay, either as a partial or full replacement for wheat straw, positively affects the immune profile and enriches beneficial bacteria.
4.1.　 Wheat straw and alfalfa hay mixture positively affect the plasma immune profile
Oral administration of 0.75 g of mannan-oligosaccharide to calves and inclusion of 3% of fermented wheat bran polysaccharides in the diet of lambs has been shown to improve the circulating IgA and IgM levels, respectively (Lazarevic et al., 2010; Wang et al., 2020), suggesting that an active component of carbohydrates can enhance humoral immunity. In this study, the increases in plasma IgA and IgG levels in response to feeding wheat straw and alfalfa hay mixture were likely due to the differences in starch and fiber components in the supplemented forages. A greater level of plasma IL-2, which has been shown to promote immunoglobulins production from the B cells (Collins and Oldham, 1993), was consistent with the higher concentration of plasma immunoglobulins of lambs from the 30M group. It can also highlight the anti-inflammatory property of IL-2 (Bachmann et al., 2007), as reported in the previous study that showed that ewes fed basal diets with rumen-protected methionine and lysine have greater expression of IL-2 in the neutrophils (Tsiplakou et al., 2018). The observed resistance to inflammation induced by a high-concentrate diet, indicated by the higher IL-2 expression and improved humoral immunity in lambs fed the two forages, suggests that incorporating wheat straw and alfalfa into a concentrate-rich diet could enhance the immune profile without adverse effects. In alignment with the observed effects on cytokines and immunoglobulins, lambs fed a diet containing wheat straw and alfalfa hay mixture showed a higher level of plasma GLP-2 than those in the other two groups. Previous research has indicated that GLP-2, stimulated by a high-energy diet, contributes to the maintenance of intestinal barrier integrity and exhibits anti-inflammatory characteristics (Sun et al., 2018). The enhancement of circulatory GLP-2 in the 30M group could be associated with the specific composition of the diet, particularly its starch, lipid, and fiber content. Overall, lambs in the 30M group had better systemic immune status than those in the other two groups. This improvement in cytokine, immunoglobulins, and GLP-2 in the 30M group was matched with the enrichment of relative abundances of bacterial genera, including the Lachno-spiraceae, Olsenella, and Eubacterium_coprostanol_genes group. The latter two genera have been shown to contribute to the immune system function (Mager et al., 2020) and lower the risk of metabolic disease by reducing the level of cholesterol, respectively (Li et al., 1998).
4.2.　 Wheat straw and alfalfa hay mixture enrich the prevalence of fiber and starch-degraded bacteria
The microbiota analysis showed that the dominant bacterial phyla, including Firmicutes, Actinobacteria, Proteobacteria, and Fibrobacteres, were shifted across the 3 dietary groups, indicating the specific preferences of bacterial phyla in the degradation of dietary fiber and fermentable substrate released from various plant polysaccharides present in the supplemented forages, which is in line with the previous report (Pandit et al., 2018). In this regard, Prevotella has the ability to utilize a wide range of substrates, such as polysaccharides and proteins (Betancur-Murillo et al., 2022). The enrichment of Prevotella_7 in the 30A and 30M groups can be associated with the content of xylan, protein, starch, and pectin in the alfalfa hay. Given the role of Lachnospiraceae in maintaining the intestinal barrier and suppressing gut inflammation via modulating the production of butyrate (Meehan and Beiko, 2014), the greater abundance of the Lachnospiraceae (NK3A20_group and unclassified) in the 30M group of the present study is correlated with the observed improvements in immune status, as evidenced by the slight increases in IgA, IgG, and IL-2 levels. Likewise, considering the roles of Eubacterium_coprostanol-genes and Olsenella in reducing cholesterol levels and improving the immune system, the enrichments of these two bacterial genera in the rumen of lambs from the 30M group are further attributed to the alteration of immune profiles.
Although the exact role of the Ruminococcaceae_NK4A214_group is not clarified so far, the majority of bacterial genera belonging to the family Ruminococcaceae have been associated with lipid metabolism through modulating rumen biohydrogenation of long-chain fatty acids (Wang et al., 2019) and degradation of fiber and plant cell-wall polysaccharides (Zhang et al., 2022). We thus speculated that a greater abundance of Ruminococcaceae_NK4A214_group in the 30S group might alter the lipid metabolism or fatty acid profiles. Succiniclasticum has a crucial role in the synthesis of ruminal propionate, a main precursor to gluconeogenesis, from succinate via the degradation of dietary fiber (van Gylswyk, 1995). Thus, the higher prevalence of Succiniclasticum in the rumen of lambs from the 30M group might improve the production of propionate. Considering the attribution of Shuttleworthia in the butyrate production (Zhou et al., 2021), the greater abundance of this bacterium in the 30M group could enhance the synthesis of butyrate, which has a crucial role in microbial and host epithelial cell growth. Overall, the enrichments of Prevotella_7, Shuttleworthia, and Lachnospiraceae (NK3A20_group and unclassified) are likely to alter rumen fermentation, thereby enriching the concentrations of butyrate, propionate, and the total VFAs, which is in line with the previous study (Mu et al., 2022).
4.3.　 The responses of various biological pathways to wheat straw, alfalfa hay, or both
To obtain clearer insights into how biological pathways in the rumen tissues of lambs respond to a high-concentrate diet supplemented with wheat straw, alfalfa hay, or both, we performed functional analysis using KEGG pathway enrichment analysis for the unique and shared DEGs. The results showed that most enriched biological pathways were associated with carbohydrate, lipid, amino acid, and immune metabolism. The PLA2G4D and PLA2G4B genes have been shown to be involved in various metabolic functions, including the regulation of prostaglandin biosyn-thesis, cellular inflammation, glycerophospholipid hydrolysis, gut membrane permeability, and oxidative stress (Tithof et al., 2007). These two shared DEGs decreased with the supplementation of wheat straw and alfalfa hay mixture or alfalfa hay alone, compared to the wheat straw alone, which agrees with the findings of Sun et al. (2021), who showed that a decrease in the expression of PLA2G4D and PLA2G4B occurred with the introduction of alfalfa hay. The complement system plays a vital role in innate immunity, serving as the first line of defense against pathogens and infection, and MASP2 and C4BPA have been reported to be involved in complement activation via the lectin pathway (García-Laorden et al., 2020). These two unique DEGs (30S vs. 30A) exhibited differing expression patterns, likely contributing to the balance of immune function in rumen tissue. Additionally, C4BPA may perform multiple functions beyond immune function in the bovine rumen, such as lipid metabolism (Iqbal et al., 2021). Supplementing alfalfa hay may improve the fatty acid and amino acid profiles, as evidenced by the greater expression of PYCR1 and C4BPA genes, which are involved in the biosynthesis of amino acids and lipid metabolism.
Given the roles of PGAM2 and HK2 in glycolysis and their facilitation of the connection between glycolysis and physiological homeostasis through various mechanisms, the upregulation of these two unique DEGs (30S vs. 30A) might indicate that glycolysis is more profoundly influenced by the wheat straw than alfalfa hay. SLC26A9 has been implicated in regulating cellular pH and interacting with VFAs (Sun et al., 2021). SLC9A3 plays a crucial role in conveying protons into the rumen and sodium into the cell, thereby keeping the homeostasis of the ruminal epithelium (Beckett et al., 2021). The upregulation of SLC26A9 and SLC9A3 in lambs fed with wheat straw and alfalfa hay might be associated with the concentrations of VFAs, which can contribute to intraepithelial homeostasis, as indicated by the greater abundance of bacterial genera involved in VFA production. Thus, the rumen maintains homeostasis by enhancing the mechanism of discharging protons into the ruminal lumen and regulating pH balance. The upregulation of ACOT7 in response to supplementing a high-concentrate diet with wheat straw, rather than a mixture of wheat straw and alfalfa hay, is partly consistent with the findings of Sun et al. (2021). Considering the roles of CARD14 and LPB genes in activating inflammatory transcription factors and the innate immune response, respectively, decreased expression of both CARD14 and LPB indicates that the lambs in the 30S group did not experience inflammation. In general, feeding either alfalfa hay or wheat straw alone likely improves various individual amino and fatty acid profiles, whereas a mixture of wheat straw and alfalfa hay enhances immune profiles and fermentation end-products, indicating the unique effects of wheat straw, alfalfa hay, or both on the molecular processes of the rumen.
4.4.　 The WGCNA uncovers the coordinated response of microbes and host transcriptomes in the rumen
The rumen tissue profoundly influences numerous metabolic functions, including lipid and amino acid metabolism, neuroendocrine regulation, and immune homeostasis. These metabolic functions are supposed to be regulated by the cross-talk between rumen tissue and microbial population and may also be altered in response to various feeding patterns. Therefore, various metabolic pathways linked with lipid and amino acid metabolism and immune homeostasis are anticipated to be annotated in response to feeding a high-concentrate diet supplemented with wheat straw, alfalfa hay, or both. In this regard, a previous study has shown that the rumen microbiota could regulate more than 10% of the host transcriptome (Malmuthuge et al., 2019). Given the enrichment of metabolic pathways related to glycolysis/gluconeogenesis and innate immunity in the rumen among genes uniquely expressed in lambs fed a high-concentrate diet supplemented with either wheat straw, alfalfa hay, or a combination of both, the gene modules showing a strong association with the microbial taxa and immune indexes may offer deeper insights into the interaction between the host transcriptome and microbiota in regulating the rumen meta-bolism. Thus, we assumed that genes involved in lipid and amino acid metabolism of rumen tissue, along with microbial taxa in the rumen content, may coordinate to regulate rumen function in response to feeding a high-concentrate diet supplemented with various forages.
Based on the WGCNA analysis, we found genes involved in the lipid, carbohydrate, and amino acid metabolism enriched in the darkgreen module, which shows both positive and negative correlations with various bacterial genera. The saccharolytic bacteria Prevotella_7 and Shuttleworthia showed a wide range of interaction with various genes from the darkgreen module, indicating these two bacterial genera might have a profound contribution to the host-microbial interaction under the present feeding conditions. Specifically, the TECR, MPI, GOT2, and CBS genes had a positive correlation with Prevotella_7, which has a vital role in the degradation of starch, protein, and xylan. The TECR gene is believed to catalyze the reduction of trans-2,3-enoyl-CoA to saturated acyl-CoA, and CBS is involved in redox homeostasis and mitochondrial bioenergetics, regulating homocysteine metabolism and biosynthesis of hydrogen sulfide (Zhu et al., 2018). GOT2 takes part in the malate-aspartate shuttle, transferring electrons produced during glycolysis into the mitochondria for terminal oxidation or ATP generation. MPI is involved in glycosylation reactions by sustaining the supply of D-mannose derivatives (Shtraizent et al., 2017). These highlight that the substrate derived from the degradation of starch, protein, and xylan might contribute to homocysteine metabolism, fatty acid elongation, and glycosylation reactions via TECR, MPI, GOT2, and CBS genes.
PYCR1 plays a crucial role in the NAD(P)H-dependent synthesis of proline from pyrroline-5-carboxylate in a NAD(P)H-dependent. GMPPB facilitates the synthesis of the GDP-mannose pyrophos-phorylase enzyme, essential for glycosylation pathways (Liu et al., 2021). PYCR1 and GMPPB showed positive correlations with Shuttleworthia, which belongs to the Ruminococcaceae family. Considering the role of Shuttleworthia in short-chain fatty acid (SCFA) production and its family composed of members with a high content of odd-chain iso-fatty acid (OCIFA), Shuttleworthia is likely involved in fructose and mannose metabolism and redox meta-bolism by influencing proline and fatty acid synthesis, which has been shown to be involved in buffering cellular redox status. ACOT8 is proposed to be involved in fatty acid oxidation and controlling peroxisomal lipid metabolism (Hunt et al., 2012). MIF has been shown to participate in inflammatory responses and maintain the gut barrier function (Sumaiya et al., 2022). MIF and ACOT8 had a positive association with the Succiniclasticum, which is believed to promote butyrate production. Butyrate has a crucial role in innate immunity, gut barrier function, and lipid metabolism. Thus, an interaction of Succiniclasticum with MIF and ACOT8, mediated by butyrate, is likely, which can significantly impact fatty acid meta-bolism and innate immunity. These results indicate the coordinated interactions between the host transcriptome and microbes in a broad spectrum of metabolic pathways, regulating the rumen function under the present feeding condition.
We also speculated that genes involved in immune function and metabolism might respond to significantly altered plasma immune indexes. The WGCNA analysis of the host transcriptome and plasma immune indexes indicated that aminoacyl-tRNA biosynthesis, the adipocytokine signaling pathway, intestinal immune network for IgA production, and AMPK signaling pathway were enriched in the darkred module, which shows positive and negative correlation with plasma immune indexes. Particularly, IgA showed a positive association with SRA1, RBM8A, and RUVBL2. The Ruvbl2 gene is reported as a prerequisite for T-dependent antibody responses as well as the development of T-cells (Arnold et al., 2012), and the expression of SRA1 is linked with obesity and inflammation (Kochumon et al., 2021). RBM8A plays a vital role in synthesizing protein (RNA-binding motif protein 8A), which participates in various cellular functions, potentially including the IgA production process. These might highlight that host genes linked to humoral and innate immunity could positively respond to the plasma immune indexes of lambs fed a high-concentrate diet with wheat straw and alfalfa hay mixture. Furthermore, GLP-2 is involved in enhancing gut barrier function, increasing epithelial proliferation, and inhibiting apoptosis. It also shows a positive association with several genes, including OAZ1 and LAMTOR2. OAZ1 has a vital role in inhibiting the synthesis of inflammatory cytokines and regulating the synthesis of polyamine, which is involved in gut barrier function (Kang et al., 2017). Meanwhile, LAMTOR2 is believed to participate in immune function by controlling endosomal biogenesis and activating ERK (Lin et al., 2019b). Taken together, the immune status of lambs fed a high-concentrate diet could be altered by supplementing wheat straw and alfalfa hay mixture, as evidenced by the improved plasma immune indexes and their positive association with the host genes involved in immune metabolism. These alterations stem from differences in the nutrient composition of wheat straw and alfalfa hay.
5.　Conclusion
Less
Feeding a high-concentrate diet with wheat straw alone is more likely to promote fatty acid and amino acid metabolism in rumen epithelial tissue. Combined supplementation of wheat straw and alfalfa hay enriches the humoral immunity and the prevalence of saccharolytic bacteria, such as Prevotella_7 and Shuttleworthia, compared to wheat straw alone. This is likely due to differences in nutrient compositions, mainly substrates derived from the degradation of polysaccharides, proteins, xylan, and some other fibers. The inclusion of alfalfa hay in a high-concentrate diet alters the fatty acid synthesis metabolism and abundance of Prevotella_7. Shuttleworthia, Prevotella_7, and Succiniclasticum have shown a wide range of interactions with the host genes involved in amino acid and fatty acid metabolism and immune function. This indicates that ruminal microbes may act as a vital driver in the microbiome-host interaction, likely by altering fermentation end-products or metabolites. These findings shed light on the possible cross-talk between microbes and host transcriptome to regulate the rumen function of lambs fed a high-concentrate diet supplemented with forages. Future studies should evaluate the long-term effects of feeding a concentrate-rich diet, formulated to supply equivalent NDF and protein, with alfalfa hay replacing wheat straw, on host-microbial interaction. This provides unique insights into the role of fiber from different forages in regulating host-microbial interaction and rumen function in lambs fed a high-concentrate diet.
References
Less
AOAC. Official methods of analysis. Gaithersburg (Maryland): The Association of Official Analytical Chemists; 2000.
Arnold CN, Pirie E, Dosenovic P, McInerney GM, Xia Y, Wang N, Li X, Siggs OM, Karlsson Hedestam GB, Beutler B. A forward genetic screen reveals roles for Nfkbid, Zeb1, and Ruvbl2 in humoral immunity. Proc Natl Acad Sci U S A 2012;109(31):12286-93. https://doi.org/10.1073/pnas.1209134109.
Bachmann MF, Wolint P, Walton S, Schwarz K, Oxenius A. Differential role of IL-2R signaling for CD8+ T cell responses in acute and chronic viral infections. Eur J Immunol 2007;37(6):1502-12. https://doi.org/10.1002/eji.200637023.
Beckett L, Gleason CB, Bedford A, Liebe D, Yohe TT, Hall MB, Daniels KM, White RR. Rumen volatile fatty acid molar proportions, rumen epithelial gene expression, and blood metabolite concentration responses to ruminally degradable starch and fiber supplies. J Dairy Sci 2021;104(8):8857-69. https://doi.org/10.3168/jds.2020-19622.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995;57(1):289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
Betancur-Murillo CL, Aguilar-Marín SB, Jovel J Prevotella. A key player in ruminal metabolism. Microorganisms 2022;11(1). https://doi.org/10.3390/microorganisms11010001.
Chai J, Lv X, Diao Q, Usdrowski H, Zhuang Y, Huang W, Cui K, Zhang N. Solid diet manipulates rumen epithelial microbiota and its interactions with host transcriptomic in young ruminants. Environ Microbiol 2021;23(11):6557-68. https://doi.org/10.1111/1462-2920.15757.
Chen H, Wang C, Huasai S, Chen A. Effects of dietary forage to concentrate ratio on nutrient digestibility, ruminal fermentation and rumen bacterial composition in Angus cows. Sci Rep 2021;11(1):17023. https://doi.org/10.1038/s41598-021-96580-5.
Collins RA, Oldham G. Recombinant human interleukin 2 induces proliferation and immunoglobulin secretion by bovine B-cells: tissue differences and preferential enhancement of immunoglobulin A. Vet Immunol Immunopathol 1993;36(1): 31-43. https://doi.org/10.1016/0165-2427(93)90004-n.
Eastridge ML, Starkey RA, Gott PN, Oelker ER, Sewell AR, Mathew B, Firkins JL. Dairy cows fed equivalent concentrations of forage neutral detergent fiber from corn silage, alfalfa hay, wheat straw, and corn stover had similar milk yield and total tract digestibility. Anim Feed Sci Technol 2017;225:81-6. https://doi.org/10.1016/j.anifeedsci.2017.01.013.
Elmhadi ME, Ali DK, Khogali MK, Wang H. Subacute ruminal acidosis in dairy herds: microbiological and nutritional causes, consequences, and prevention strategies. Anim Nutr 2022;10:148-55. https://doi.org/10.1016/j.aninu.2021.12.008.
Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, Ravel J. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014;2(1):6. https://doi.org/10.1186/2049-2618-2-6.
García-Laorden MI, Hernández-Brito E, Muñoz-Almagro C, Pavlovic-Nesic S, Rúa-Figueroa I, Briones ML, Rajas O, Borderías L, Payeras A, Lorente L, et al. Should MASP-2 deficiency Be considered a primary immunodeficiency? Relevance of the lectin pathway. J Clin Immunol 2020;40(1):203-10. https://doi.org/10.1007/s10875-019-00714-4.
Gebeyew K, Yang C, He Z, Tan Z. Low-protein diets supplemented with methionine and lysine alter the gut microbiota composition and improve the immune status of growing lambs. Appl Microbiol Biotechnol 2021;105(21–22):8393-410. https://doi.org/10.1007/s00253-021-11620-4.
Heuzé VTG, Lebas F. Beet molasses. Feedipedia, a programme by INRAE, CIRAD, AFZ, and FAO. 2019. https://www.feedipedia.org/node/711.
Hunt MC, Siponen MI, Alexson SE. The emerging role of acyl-CoA thioesterases and acyltransferases in regulating peroxisomal lipid metabolism. Biochim Biophys Acta 2012;1822(9):1397-410. https://doi.org/10.1016/j.bbadis.2012.03.009.
Iqbal A, Ziyi P, Yu H, Jialing L, Haochen W, Jing F, Ping J, Zhihui Z. C4BPA: a novel Co-regulator of immunity and fat metabolism in the bovine mammary epithelial cells. Front Genet 2021;12:830566. https://doi.org/10.3389/fgene.2021.830566.
Kahyani A, Ghorbani GR, Alikhani M, Ghasemi E, Sadeghi-Sefidmazgi A, Beauchemin KA, Nasrollahi SM. Performance of dairy cows fed diets with similar proportions of undigested neutral detergent fiber with wheat straw substituted for alfalfa hay, corn silage, or both. J Dairy Sci 2019;102(12): 10903-15. https://doi.org/10.3168/jds.2019-16869.
Kang B, Jiang D, He H, Ma R, Yi Z, Chen Z. Characterization of OAZ1 and its potential functions in goose follicular development. Electron J Biotechnol 2017;26:1-6. https://doi.org/10.1016/j.ejbt.2016.12.004.
Khafipour E, Li S, Plaizier JC, Krause DO. Rumen microbiome composition determined using two nutritional models of subacute ruminal acidosis. Appl Environ Microbiol 2009;75(22):7115-24. https://doi.org/10.1128/aem.00739-09.
Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 2015;12(4):357-60. https://doi.org/10.1038/nmeth.3317.
Kochumon S, Arefanian H, Sindhu S, Shenouda S, Thomas R, Al-Mulla F, Tuomilehto J, Ahmad R. Adipose tissue steroid receptor RNA activator 1 (SRA1) expression is associated with obesity, insulin resistance, and inflammation. Cells 2021;10(10). https://doi.org/10.3390/cells10102602.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 2008;9:559. https://doi.org/10.1186/1471-2105-9-559.
Lascano GJ, Heinrichs AJ. Effects of feeding different levels of dietary fiber through the addition of corn stover on nutrient utilization of dairy heifers precision-fed high and low concentrate diets. J Dairy Sci 2011;94(6):3025-36. https://doi.org/10.3168/jds.2010-3831.
Lazarevic M, Shabanovic M, Spring P, Tokic V, Tucker LA. Effect of gut active carbohydrates on plasma IgG concentrations in piglets and calves. Animal 2010;4(6):938-43. https://doi.org/10.1017/S1751731110000194.
Lee SA, Kong C, Adeola O, Kim BG. Different coefficients and exponents for meta-bolic body weight in a model to estimate individual feed intake for growing-finishing pigs. Asian-Australas J Anim Sci 2016;29(12):1756-60. https://doi.org/10.5713/ajas.16.0420.
Li L, Batt SM, Wannemuehler M, Dispirito A, Beitz DC. Effect of feeding of a cholesterol-reducing bacterium, Eubacterium coprostanoligenes, to germ-free mice. Lab Anim Sci 1998;48(3):253-5.
Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics 2008;24(5):713-4. https://doi.org/10.1093/bioinformatics/btn025.
Lin L, Xie F, Sun D, Liu J, Zhu W, Mao S. Ruminal microbiome-host crosstalk stimulates the development of the ruminal epithelium in a lamb model. Microbiome 2019a;7(1):83. https://doi.org/10.1186/s40168-019-0701-y.
Lin C, Nozawa T, Minowa-Nozawa A, Toh H, Aikawa C, Nakagawa I. LAMTOR2/ LAMTOR1 complex is required for TAX1BP1-mediated xenophagy. Cell Microbiol 2019b;21(4):e12981. https://doi.org/10.1111/cmi.12981.
Liu Z, Wang Y, Yang F, Yang Q, Mo X, Burstein E, Jia D, Cai XT, Tu Y. GMPPB-congenital disorders of glycosylation associate with decreased enzymatic activity of GMPPB. Mol Biomed 2021;2(1):13. https://doi.org/10.1186/s43556-021-00027-2.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.
Mager LF, Burkhard R, Pett N, Cooke NCA, Brown K, Ramay H, Paik S, Stagg J, Groves RA, Gallo M, et al. Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy. Science 2020;369(6510):1481-9. https://doi.org/10.1126/science.abc3421.
Malmuthuge N, Liang G, Guan LL. Regulation of rumen development in neonatal ruminants through microbial metagenomes and host transcriptomes. Genome Biol 2019;20(1):172. https://doi.org/10.1186/s13059-019-1786-0.
McCann JC, Luan S, Cardoso FC, Derakhshani H, Khafipour E, Loor JJ. Induction of subacute ruminal acidosis affects the ruminal microbiome and epithelium. Front Microbiol 2016;7:701. https://doi.org/10.3389/fmicb.2016.00701.
Meehan CJ, Beiko RG. A phylogenomic view of ecological specialization in the Lachnospiraceae, a family of digestive tract-associated bacteria. Genome Biol Evol 2014;6(3):703-13. https://doi.org/10.1093/gbe/evu050.
Mu Y, Qi W, Zhang T, Zhang J, Mao S. Multi-omics analysis revealed coordinated responses of rumen microbiome and epithelium to high-grain-induced subacute rumen acidosis in lactating dairy cows. mSystems 2022;7(1): e0149021. https://doi.org/10.1128/msystems.01490-21.
Nishihara K, van Niekerk J, Innes D, He Z, Cánovas A, Guan LL, Steele M. Transcriptome profiling revealed that key rumen epithelium functions change in relation to short-chain fatty acids and rumen epithelium-attached microbiota during the weaning transition. Genomics 2023;115(5):110664. https://doi.org/10.1016/j.ygeno.2023.110664.
Pandit RJ, Hinsu AT, Patel SH, Jakhesara SJ, Koringa PG, Bruno F, Psifidi A, Shah SV, Joshi CG. Microbiota composition, gene pool and its expression in Gir cattle (Bos indicus) rumen under different forage diets using metagenomic and meta-transcriptomic approaches. Syst Appl Microbiol 2018;41(4):374-85. https://doi.org/10.1016/j.syapm.2018.02.002.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013;41(Database issue):D590-6. https://doi.org/10.1093/nar/gks1219.
Shtraizent N, DeRossi C, Nayar S, Sachidanandam R, Katz LS, Prince A, Koh AP, Vincek A, Hadas Y, Hoshida Y, et al. MPI depletion enhances O-GlcNAcylation of p53 and suppresses the Warburg effect. Elife 2017;6. https://doi.org/10.7554/eLife.22477.
Sumaiya K, Langford D, Natarajaseenivasan K, Shanmughapriya S. Macrophage migration inhibitory factor (MIF): a multifaceted cytokine regulated by genetic and physiological strategies. Pharmacol Ther 2022;233:108024. https://doi.org/10.1016/j.pharmthera.2021.108024.
Sun D, Li H, Mao S, Zhu W, Liu J. Effects of different starch source of starter on small intestinal growth and endogenous GLP-2 secretion in preweaned lambs. J Anim Sci 2018;96(1):306-17. https://doi.org/10.1093/jas/skx029.
Sun D, Yin Y, Guo C, Liu L, Mao S, Zhu W, Liu J. Transcriptomic analysis reveals the molecular mechanisms of rumen wall morphological and functional development induced by different solid diet introduction in a lamb model. J Anim Sci Biotechnol 2021;12(1):33. https://doi.org/10.1186/s40104-021-00556-4.
Tithof PK, Roberts MP, Guan W, Elgayyar M, Godkin JD. Distinct phospholipase A2 enzymes regulate prostaglandin E2 and F2alpha production by bovine endometrial epithelial cells. Reprod Biol Endocrinol 2007;5:16. https://doi.org/10.1186/1477-7827-5-16.
Tsiplakou E, Mavrommatis A, Skliros D, Sotirakoglou K, Flemetakis E, Zervas G. The effects of dietary supplementation with rumen-protected amino acids on the expression of several genes involved in the immune system of dairy sheep. J Anim Physiol Anim Nutr 2018;102(6):1437-49. https://doi.org/10.1111/jpn.12968.
van Gylswyk NO. Succiniclasticum ruminis gen. nov., sp. nov., a ruminal bacterium converting succinate to propionate as the sole energy-yielding mechanism. Int J Syst Bacteriol 1995;45(2):297-300. https://doi.org/10.1099/00207713-45-2-297.
Van Soest PJ, Robertson JB, Lewis BA. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci 1991;74(10):3583-97. https://doi.org/10.3168/jds.S0022-0302(91)78551-2.
Wang X, Martin GB, Wen Q, Liu S, Zhang J, Yu Y, Shi B, Guo X, Zhao Y, Yan S. Linseed oil and heated linseed grain supplements have different effects on rumen bacterial community structures and fatty acid profiles in cashmere kids1. J Anim Sci 2019;97(5):2099-113. https://doi.org/10.1093/jas/skz079.
Wang Y, Wang R, Hao X, Hu Y, Guo T, Zhang J, Wang W, Shi X, An X, Qi J. Growth performance, nutrient digestibility, immune responses and antioxidant status of lambs supplemented with humic acids and fermented wheat bran polysaccharides. Anim Feed Sci Technol 2020;269:114644. https://doi.org/10.1016/j.anifeedsci.2020.114644.
Yang C, Zhang T, Tian Q, Cheng Y, Gebeyew K, Liu G, Tan Z, He Z. Supplementing mannan oligosaccharide reduces the passive transfer of immunoglobulin G and improves antioxidative capacity, immunity, and intestinal microbiota in neonatal goats. Front Microbiol 2021;12:795081. https://doi.org/10.3389/fmicb.2021.795081.
Zhang H, Lang X, Zhang Y, Wang C. Distribution of bacteria in different regions of the small intestine with Zanthoxylum bungeanum essential oil supplement in small-tailed Han sheep. Front Microbiol 2022;13:1062077. https://doi.org/10.3389/fmicb.2022.1062077.
Zhou M, Ghoshal B, Stothard P, Guan LL. Distinctive roles between rumen epimural and content bacterial communities on beef cattle feed efficiency: a combined analysis. Curr Res Microb Sci 2021;2:100085. https://doi.org/10.1016/j.crmicr.2021.100085.
Zhu H, Blake S, Chan KT, Pearson RB, Kang J. Cystathionine β-synthase in physiology and cancer. BioMed Res Int 2018;2018:3205125. https://doi.org/10.1155/2018/3205125.
Appendix
Less
Year 2025 volume 20 Issue 1
PDF
48
27
Cite this Article
BibTeX
Article Info
doi: 10.1016/j.aninu.2024.08.010
  • Receive Date：2023-09-25
  • Online Date：2026-01-28
  • Published：2025-03-10
Article Data
Affiliations
History
  • Received：2023-09-25
  • Revised：2024-05-02
  • Accepted：2024-08-23
Affiliations
    aCAS Key Laboratory for Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
    bUniversity of Chinese Academy of Sciences, Beijing 100049, China
    cInner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot 010031, China
    dDepartment of Animal Sciences, Faculty of Agriculture, Wollega University, Ethiopia

Corresponding:

*

Corresponding author. E-mail address: (Z. He).
References
Share
https://castjournals.cast.org.cn/joweb/aninu/EN/10.1016/j.aninu.2024.08.010
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House